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DP-090 - Implementing a Machine Learning Solution with Microsoft Azure Databricks

Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this one-day certification & training course, you'll learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

Duration: 1.0 day

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Schedule

Kuala Lumpur | 01 Dec 2023 - 01 Dec 2023 Malaysia
Virtual ILT | 01 Dec 2023 - 01 Dec 2023 Malaysia

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Objectives

  • Provision an Azure Databricks workspace.
  • Identify core workloads and personas for Azure Databricks.
  • Describe key concepts of an Azure Databricks solution.
  • Describe key elements of the Apache Spark architecture.
  • Create and configure a Spark cluster.
  • Describe use cases for Spark.
  • Use Spark to process and analyze data stored in files.
  • Use Spark to visualize data.
  • Prepare data for machine learning
  • Train a machine learning model
  • Evaluate a machine learning model
  • Use MLflow to log parameters, metrics, and other details from experiment runs.
  • Use MLflow to manage and deploy trained models.
  • Describe Azure Machine Learning
  • Run Azure Databricks experiments in Azure Machine Learning
  • Log metrics in Azure Machine Learning with MLflow
  • Run Azure Machine Learning pipelines on Azure Databricks compute
  • Describe considerations for model deployment
  • Plan for Azure Machine Learning deployment endpoints
  • Deploy a model to Azure Machine Learning
  • Troubleshoot model deployment

Content

1. Explore Azure Databricks

Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.

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2. Use Apache Spark in Azure Databricks

Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.

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3. Train a machine learning model in Azure Databricks

Machine learning involves using data to train a predictive model. Azure Databricks support multiple commonly used machine learning frameworks that you can use to train models.

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4. Use MLflow in Azure Databricks

MLflow is an open source platform for managing the machine learning lifecycle that is natively supported in Azure Databricks.

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5. Track Azure Databricks experiments in Azure Machine Learning

Azure Machine Learning is a scalable cloud platform for training, deploying, and managing machine learning solutions.

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6. Deploy Azure Databricks models in Azure Machine Learning

You can use Azure Databricks to train machine learning models, and deploy the trained models in Azure Machine Learning endpoints.

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Audience

This course is designed for data scientists with experience of Pythion who need to learn how to apply their data science and machine learning skills on Azure Databricks.

Prerequisites

Before attending this course, you should have experience of using Python to work with data, and some knowledge of machine learning concepts. Before attending this course, complete the following learning path on Microsoft Learn:

  • Create machine learning models

Certification

product-certification
This course is not associated with any Certification.

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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